Using self-supervised learning, the authors pre-trained a large model using one millon unlabelled molecules from ChEMBL. This model can subsequently be fine-tuned for various QSAR tasks. Here, we provide the encodings for the molecular structures using the pre-trained model, not the fine-tuned QSAR models.
- EOS model ID:
eos9zw0 - Slug:
molpmofit
- Input:
Compound - Input Shape:
Single - Task:
Representation - Output:
Other value - Output Type:
Float - Output Shape:
Matrix - Interpretation: Embedding vectors of each smiles are obtained, represented in a matrix, where each row is a vector of embedding of each smiles character, with a dimension of 400. The pretrained model is loaded using the fastai library
- Publication
- Source Code
- Ersilia contributor: GemmaTuron
If you use this model, please cite the original authors of the model and the Ersilia Model Hub.
This package is licensed under a GPL-3.0 license. The model contained within this package is licensed under a CC license.
Notice: Ersilia grants access to these models 'as is' provided by the original authors, please refer to the original code repository and/or publication if you use the model in your research.
The Ersilia Open Source Initiative is a Non Profit Organization (1192266) with the mission is to equip labs, universities and clinics in LMIC with AI/ML tools for infectious disease research.
Help us achieve our mission!